@inproceedings{liu-etal-2019-discourse,
title = "Discourse Representation Parsing for Sentences and Documents",
author = "Liu, Jiangming and
Cohen, Shay B. and
Lapata, Mirella",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1629",
doi = "10.18653/v1/P19-1629",
pages = "6248--6262",
abstract = "We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.",
}
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%0 Conference Proceedings
%T Discourse Representation Parsing for Sentences and Documents
%A Liu, Jiangming
%A Cohen, Shay B.
%A Lapata, Mirella
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F liu-etal-2019-discourse
%X We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993). Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents. We present a general framework for parsing discourse structures of arbitrary length and granularity. We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy. Experimental results on sentence- and document-level benchmarks show that our model outperforms competitive baselines by a wide margin.
%R 10.18653/v1/P19-1629
%U https://aclanthology.org/P19-1629
%U https://doi.org/10.18653/v1/P19-1629
%P 6248-6262
Markdown (Informal)
[Discourse Representation Parsing for Sentences and Documents](https://aclanthology.org/P19-1629) (Liu et al., ACL 2019)
ACL